Recognizing and Analyzing Ball Screen Defense in the NBA

Abstract: Finding an effective counter to the ball screen is a high priority for NBA coaching staffs. I n this paper, we present construction and application of a tool for automatically recognizing common defensive counters to ball screens. Using player tracking data and supervised machine learning techniques, we learn a classifier that labels ball screens by how they were defended. Applied to a selection of games over four seasons, our classifier identified and labeled 270,823 attempts to defend a ball screen. At the team level, we identify outliers who favored a particular defensive scheme on the way to successful seasons. F or example, the ’12-13’ Bulls went “over” 7% more often than the average team that year. For players, we examine both offense and defense. Offensively, we report how often players face a given defense and their effectiveness in creating points from those situations. Notably, Damian Lillard sees defenders go over in ⅔ of his screens, but with 0.84 pts/poss he’s among the league’s best at capitalizing on these opportunities. Defensively, we examine pairs of players and their ability to stifle opponent scoring. In ’13-14’, Dwight Howard and Jeremy Lin were particularly effective when Lin went over screens, holding the offense to just 0.27 pts/poss. This fully automated tool opens the door to analysis of defensive tactics at an unprecedented scale.